TSPred (version 5.1)

mlm_io: Subset sliding windows of data

Description

Function subsets sliding windows of data into input and output datasets to be passed to machine-learning methods.

Usage

mlm_io(sw)

Arguments

sw

A numeric matrix with sliding windows of time series data as returned by sw.

Value

A list with input and output datasets.

Details

When sw has k columns (sliding windows of size k), the input dataset contains the first k-1 columns and the output dataset contains the last column of data.

References

E. Ogasawara, L. C. Martinez, D. De Oliveira, G. Zimbrao, G. L. Pappa, and M. Mattoso, 2010, Adaptive Normalization: A novel data normalization approach for non-stationary time series, Proceedings of the International Joint Conference on Neural Networks.

See Also

Other transformation methods: Diff(), LogT(), WaveletT(), emd(), mas(), outliers_bp(), pct(), train_test_subset()

Examples

# NOT RUN {
data(CATS)
swin <- sw(CATS[,1],5)
d <- mlm_io(swin)

# }